Mediation of traumatic brain injury

ABSTRACT

Provided are systems, methods, and devices for providing mediation of a traumatic brain injury. Systems may include an interface, processing devices, and a controller. The interface is configured to obtain measurements from a brain of a user with a traumatic brain injury. A first processing device is configured to generate multiple brain state parameters characterizing one or more features of a brain state of the user. A second processing device is configured to generate models of the brain of the user based on the plurality of brain state parameters and the plurality of measurements, and determine, using the models and training data comprising one or more mediation data points, a mediation procedure for reducing one or more symptoms of the traumatic brain injury. The mediation procedure is provided to one or more entities, and one or more control signals are generated by the controller based on the mediation procedure.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/170,675 entitled MEDIATION OF TRAUMATIC BRAIN INJURY (Attorney DocketNo. STIMP003) filed on Oct. 25, 2018, which claims priority under 35U.S.C. § 119(e) to U.S. Provisional Application No. 62/579,300, filedOct. 31, 2017, entitled MEDIATION OF TRAUMATIC BRAIN INJURY, thecontents of each of which are hereby incorporated by reference in theirentirety for all purposes.

TECHNICAL FIELD

The present disclosure relates generally to brain activity, and morespecifically to providing mediation of traumatic brain injury.

DESCRIPTION OF RELATED ART

A human brain may include neurons which exhibit measurable electricalsignals when active. Accordingly, various measuring modalities, such aselectrodes, may be used to measure such electrical activity. The neuralactivity of neurons may include a variety of components. In someexamples, such electrical activity may be measured and represented as apower spectrum in a frequency domain. Moreover, different behaviors orpatterns in different frequency bands may be identified and referred toas particular types of waves, such as delta and theta waves.

SUMMARY

Provided are systems, methods, and devices for providing mediation oftraumatic brain injury.

In one aspect, systems may comprise an interface configured to obtain aplurality of measurements from a brain of a user with a traumatic braininjury, and a first processing device comprising one or more processorsconfigured to generate a plurality of brain state parameterscharacterizing one or more features of at least one brain state of thebrain of the user. The systems may further comprise a second processingdevice including one or more processors configured to: generate one ormore models of the brain of the user based, at least in part, on theplurality of brain state parameters and the plurality of measurements;determine, using the models and training data comprising one or moremediation data points, a procedure for mediation configured to reduceone or more symptoms of the traumatic brain injury; and provide theprocedure for mediation to one or more entities. The systems may furthercomprise a controller comprising one or more processors configured togenerate one or more control signals based on the procedure formediation.

The plurality of measurements represents neural activity of the brainover a given period of time. The plurality of brain state parameters mayinclude deterministic and stochastic observers and estimators of one ormore brain states. The plurality of brain state parameters may include alearning estimator model configured to estimate the effects ofbehavioral or functional responses.

The second processing device may further be configured to: generatediagnostic information based on the one or more models; and transmit anotification message including the diagnostic information to the one ormore entities.

The one or more control signals may be transmitted to the interface andthe interface may be further configured to generate one or more stimulibased on the one or more control signals.

The systems may further comprise an embedded prosthetic device. The oneor more control signals may be transmitted to the embedded prostheticdevice and the embedded prosthetic device may be configured to performan operation based on the control signal. In particular embodiments, theembedded prosthetic device is associated with epileptic seizures.

The one or more entities may include a client device corresponding to amedical professional.

Other implementations of this disclosure include corresponding methodsfor providing mediation of traumatic brain injury. For instance,provided methods may comprise obtaining a plurality of measurements, viaan interface, from a brain of a user with a traumatic brain injury. Themethods further comprise generating, via a first processing devicecomprising one or more processors, a plurality of brain state parameterscharacterizing one or more features of at least one brain state of theuser.

The methods further comprise, via a second processing device comprisingone or more processors: generating one or more models of the brain ofthe user based, at least in part, on the plurality of brain stateparameters and the plurality of measurements; determining, using atleast the one or more models of the brain of the user and training datacomprising one or more mediation data points, a procedure for mediationconfigured to reduce one or more symptoms of the traumatic brain injury;and providing the procedure for mediation to one or more entities. Themethods further comprise generating one or more control signals, via acontroller comprising one or more processors, based on the procedure formediation.

The methods may further comprise transmitting the one or more controlsignals to the interface and generating, via the interface, one or morestimuli based on the one or more control signals. The methods mayfurther comprise transmitting the one or more control signals to anembedded prosthetic device, wherein the embedded prosthetic device isconfigured to perform an operation based on the one or more controlsignals.

Other implementations of this disclosure include corresponding devices,systems, computer programs, and non-transitory computer readable mediaconfigured to perform the described methods.

This and other embodiments are described further below with reference tothe figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a system for providing mediation oftraumatic brain injury, configured in accordance with some embodiments.

FIG. 2 illustrates a flowchart of an example of a method for providingmediation of traumatic brain injury, implemented in accordance with someembodiments.

FIG. 3 illustrates a flowchart of an example of a method for statusmonitoring and diagnostics, implemented in accordance with someembodiments.

FIG. 4 illustrates a flowchart of an example of a method for control ofa prosthetic, implemented in accordance with some embodiments.

FIG. 5 illustrates a flowchart of an example of a method for providingmediation of traumatic brain injury, implemented in accordance with someembodiments.

FIG. 6 illustrates an example of a computer system that can be used withvarious embodiments.

DETAILED DESCRIPTION

Reference will now be made in detail to some specific examples of theinvention including the best modes contemplated by the inventors forcarrying out the invention. Examples of these specific embodiments areillustrated in the accompanying drawings. While the present disclosureis described in conjunction with these specific embodiments, it will beunderstood that it is not intended to limit the invention to thedescribed embodiments. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the invention as defined by the appended claims.In addition, although many of the components and processes are describedbelow in the singular for convenience, it will be appreciated by one ofskill in the art that multiple components and repeated processes canalso be used to practice the techniques of the present disclosure.

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the present invention.Particular embodiments of the present invention may be implementedwithout some or all of these specific details. In other instances, wellknown process operations have not been described in detail in order notto unnecessarily obscure the present invention.

Traditional neuronal signal modeling mechanisms have significantlimitations. Available brain signal decoding mechanisms only directlymeasure simple signatures of behavior like the increase or decrease ofalpha desynchronization. However, such techniques do not estimate a realstate to be modified. For example, alpha desynchrony may just be overallarousal state. Other traditional systems like univariate and unimodalsystems are not able to accurately model complicated neural systems.Such simple models do not account for cross impacts of sub-systems ormultiple modalities of measurements, and are not able to detect oridentify various states or parameters that are to be monitored andcontrolled. Some other traditional techniques are open loop techniqueswith electrical or magnetic stimulation of different regions with manualtuning and long term behavior tracking that are not only inefficient butcan also be erroneous. Such techniques may result in over-stimulationand/or under-stimulation.

Various embodiments disclosed herein provide the ability to obtainmeasurements from a brain of a user, and generate various brain stateparameters characterizing one or more features of at least one brainstate of the user, as well as various models of the brain of the user.Such brain state parameters implemented in conjunction with thegenerated models provide a closed loop adaptive therapeutic system thatmay obtain measurements, generate a generalized or user specificfunctional and/or structural model of the brain, estimate desired brainsignatures and states, generate control signals to obtain such desiredbrain states, and obtain additional measurements if appropriate tocontinuously adjust the signals and the models. In this way, variousembodiments disclosed herein provide therapeutic and cognitivemodulation techniques that are adaptive closed loop techniques thatprovide, among other things, desired modulations with increasedefficiency and efficacy.

Furthermore, embodiments disclosed herein provide the applicability ofvarious machine learning and artificial intelligence modalities to brainstimulation in a closed loop manner, in which inputs and outputs may beprovided to and received from the brain in an adaptive and dynamicmanner. As will also be discussed in greater detail below, improvementsto augmented reality and virtual reality are also provided by enhancingperception associated with such technical fields via brain stimulation.Also discussed in greater detail below are improvements that facilitatethe implementation of hybrid therapies that may include a combination ofpharmaceutical agents as well as brain stimulation. In some embodiments,such hybrid therapies are implemented in a closed loop manner. Invarious embodiments, cognitive conditions and/or impairments may bemitigated and/or treated via brain stimulation. For example, conditionssuch as depression may be treated. In another example, cognitive declineassociated with aging may be treated. Epileptic and non-epilepticseizures, traumatic brain injury, and post-concussive symptoms may bemitigated and/or treated as well.

Further embodiments disclosed herein provide the applicability ofvarious machine learning and artificial intelligence modalities toproviding mediation of traumatic brain injury. In some embodiments,machine learning and artificial intelligence modalities are utilized fordetermining and providing a procedure for mediation for theadministration of brain stimulation following the onset of a neuralcondition, as well as the frequency of brain stimulation sufficient formitigation and/or treatment of the condition. For example, following atraumatic brain injury (TBI) leading to post-concussive symptoms,machine learning techniques may be utilized to systematically determine,given one or more sets of data relating to past brain stimulation timeframes for the same condition, the most effective and optimal procedurefor mediation of a traumatic brain injury, including a schedule oroptimal time frame for administering brain stimulation in order todecrease the negative post-concussive symptoms resulting from the braininjury.

FIG. 1 illustrates an example of a system for providing closed loopcontrol in treatments and cognitive enhancements, configured inaccordance with some embodiments. In some embodiments, system 100includes an interface, such as interface 102. In various embodiments,interface 102 is a brain interface that is configured to be coupled witha brain, such as brain 101. As will be discussed in greater detailbelow, such coupling may provide bidirectional communication, or may beused for various sensing modalities. In some embodiments, interface 102includes various electrodes, as may be included in an electrode array.Such electrodes may be included in a scalp potentialelectroencephalogram (EEG) array, may be deep brain stimulation (DBS)electrodes, or may be an epidural grid of electrodes. In other examples,interface 102 may include optogenetics mechanisms for monitoring variousneuronal processes. Mechanisms may be used to make various measurementsand acquire measurement signals corresponding to neural activity. Asused herein, neural activity may refer to spiking or non-spikingactivity/potentiation.

In various embodiments, such measured signals may be electrical signalsderived based on neural activity that may occur in cortical tissue of abrain. Such measurements may be acquired and represented in a timedomain and/or frequency domain. In this way, neural activity may bemonitored and measured over one or more temporal windows, and suchmeasurements may be stored and utilized by system 100. In variousembodiments, such neural activity may be observed for particular regionsof cortical tissue determined, at least in part, based on aconfiguration of interface 102. In one example, this may be determinedbased on a configuration and location of electrodes included ininterface 102 and coupled with the brain.

According to some embodiments, one or more components of interface 102are configured to provide stimuli to the brain coupled with interface102. For example, one or more electrodes included in interface 102 maybe configured to provide electrical stimuli to cortical tissue of thebrain. As discussed above, such electrodes may be implemented utilizingone or more of various modalities which may be placed on a user's scalp,or implanted in the user's brain.

As will be discussed in greater detail below, such actuation and stimuliprovided by interface 102 may be of many different modalities. Forexample, stimuli may be aural, visual, and/or tactile as well as beingelectrical and/or magnetic, or any suitable combination of these.Accordingly, interface 102 may further include additional components,such as speakers, lights, display screens, and mechanical actuators thatare configured to provide one or more of aural, visual, and/or tactilestimuli to a user. In this way, any suitable combination of differentmodalities may be used. For example, a combination of electrical andaural stimuli may be provided via interface 102. Further still,interface 102 may include different portions corresponding to signalacquisition and stimuli administration. For example, a first portion ofinterface 102 may include electrodes configured to measure neuralactivity, while a second portion of interface 102 includes speakersconfigured to generate aural stimuli. In various embodiments, the visualand/or auditory stimuli may be provided via one or more communicationschannels that are configured to provide such stimuli. For example, suchstimuli may be provided via a streaming media service or a socialnetworking service. In one example, such stimuli may be provided via adedicated YouTube channel that is streamed to interface 102.

In some embodiments, interface 102 further includes one or morededicated processors and an associated memory configured to obtain andstore the measurements acquired at interface 102. In this way, suchmeasurements may be stored and made available to other system componentswhich may be communicatively coupled with interface 102.

System 100 further includes first processing device 104 which isconfigured to generate brain state parameters that may characterize andidentify features of brain states and generate estimations of brainstate signatures. In various embodiments, a brain state may refer to oneor more identified patterns of neural activity. Accordingly, such brainstates may be one or more identified patterns, such as oscillation orfluctuation of activity at a particular frequency band, such as lowoscillatory behavior as well as delta, theta, alpha, beta, and gammawaves. Furthermore, such brain states may be identified based oncoupling between patterns of neural activity. For example, a brain statemay be identified based on oscillation or fluctuation of activity at aparticular frequency band, and an increase of activity in another. Otherbrain states may correspond to phase resets in prefrontal and cingulateareas. Phase resets may correspond to coherent activity in widespreadcortical regions and impact timing of neuronal activity. Activitypatterns in the prefrontal cortex can be monitored, identified, andcontrolled for associations with particular behaviors including goaldirected behavior. Neuronal synchronization and desynchronization may bedetected and managed using closed loop control based on intelligent andcontinuously adaptive neurological models. As will be discussed ingreater detail below, such identification may be implemented based, atleast in part, on various parameters, such as observers and estimators.

Accordingly, first processing device 104 is configured to generate oneor more particular observers and/or estimators that may form the basisof identification and estimation of brain states, described in greaterdetail below. As discussed above, first processing device 104 may beconfigured to generate deterministic and stochastic observers andestimators of brain states based on acquired measurements. Suchdeterministic observers may provide robustness to exogenousdisturbances, while such stochastic estimators may provide robustnessregarding noise. For example, first processing device 104 may beconfigured to implement linear and nonlinear observation and estimationmodalities such as luenberger, kalman, sliding mode, and benes filters.The application of such observation and estimation modalities may beused to generate and infer one or more parameters associated with brainstates. For example, such parameters may identify aspects of particularbrain states, such as an oscillation or resonance frequency as well as acoupling and/or weighting factor associated with one or more other brainstates. In a specific example, a condition of Schizophrenia may bemodeled as a pair of oscillators, each being an oscillating neuralpattern, and first processing device 104 may be configured to identifyand determine resonance frequencies and coupling factors associated withthe oscillators based on the previously described acquired measurements.

Thus, according to various embodiments, first processing device 104 maybe configured to implement direct and indirect state signatureestimation. First processing device 104 may also be configured toimplement brain system model parameter identification and adaptation.Furthermore, first processing device 104 may also be configured togenerate an estimation of the stability of underlying hidden states, aswell as noise estimation and rejection in underlying measurements. Invarious embodiments, first processing device 104 may also be configuredto implement cognitive relevance based measurement window sizing.Accordingly, measurement windows may be sized based on cognitiverelevance measures, and may be sized dynamically.

In some embodiments, first processing device 104 is configured toimplement baseline estimation and removal which may enhance sensitivityregarding signatures/events. As discussed above, neural activity may bemeasured and represented in a time domain and/or a frequency domain. Inone example, when represented in a frequency domain, the neural activitymay be represented as a power spectrum that follows a plot that islogarithmic or non-linear. Accordingly, in various embodiments, firstprocessing device 104 is configured to implement one or morecurve-fitting modalities to estimate a baseline of the plot, and removesuch baseline to provide a more accurate representation of more granularfeatures of the measured signal. Such granular features may berepresented with greater accuracy, and be used to identify parameters ofbrain states with greater accuracy.

First processing device 104 may also be configured to implement learningestimator models that learn state changes and estimate them. Suchlearning estimator models may also learn changing system parameters, andestimate the improvement/retrograde of behavioral/functional responses.

As similarly discussed above, observers and estimators may be used toidentify and/or infer state signatures and parameters associated withbrain states. For example, examples of brain state signatures mayinclude certain lower frequency oscillations mediated with or coupled tohigher frequency oscillations (delta to alpha, alpha to gamma, theta tomu, alpha to high frequency band) that correspond with various levels ofcognitive ability and various types of cognitive conditions to detectand identify signatures indicative of particular types of cognitiveperformance or cognitive conditions.

According to various embodiments, slow wave coupled spindle synchronyduring sleep may be used as a signature of memory retention andconsolidation. In particular embodiments, a closed loop control systemmay be configured to monitor and improve slow wave coupled spindlesynchrony. In another example, first processing device 104 is configuredto detect multiplexed parallel delta to theta, alpha to beta,delta/theta/alpha to high-frequency-band coupling to identify asignature of working memory. In yet another example, first processingdevice 104 is configured to monitor and/or control a baseline rate ofexponential decay in a spectral composition of neural activity, as wellas deviations from the baseline to identify and predict a cognitive andarousal state, such as an inhibitory or excitatory state. In variousembodiments, first processing device 104 may also be configured tomonitor beta synchronization or desynchronization to identify asignature of motor activity intent. In this way, any number of brainstates and associated signature and parameters may be identified andestimated by first processing device 104. In some embodiments, firstprocessing device 104 may be configured to identify an “activity silent”mode associated with a user in which a measure of activity is measuredand tracked as a mental state shift indicator.

System 100 further includes second processing device 106 which isconfigured to generate functional and structural models of the braincoupled with interface 102. In various embodiments, second processingdevice 106 is further configured to provide brain functional modelidentification and adaptive learning, as well as brain structural modelswith adaptive learning. In various embodiments, second processing device106 is communicatively coupled with interface 102, first processingdevice 104, as well as controller 108 discussed in greater detail below.Accordingly, second processing device 106 may receive input signals fromone or more other system components and utilize such inputs to formand/or update functional and structural models of the brain coupled withinterface 102. In various embodiments, second processing device 106 mayalso be configured to pre-process inputs received from interface 102 andfirst processing device 104 to generate one or more composite inputs.

In various embodiments, second processing device 106 is configured togenerate functional input-output univariate and multivariate models thatmay be configured to approximate at least some of the input-outputbehavior of the brain coupled with interface 102 described above. Insome embodiments, second processing device 106 is also configured toimplement adaptive learning brain models that may iteratively update andimprove the functional model of the brain that has been constructed.

In some embodiments, second processing device 106 is configured toimplement deep/machine learning and data mining based system models.Accordingly, second processing device 106 may be configured to implementone or more artificial neural networks that may be configured to modeltasks or cognitive functions of the brain. Such neural networks may beimplemented in a hierarchical manner. Moreover, such neural networks maybe trained based on signals received from other components of system100. For example, models may be trained based on inputs provided tointerface 102 from controller 108 and outputs measured via interface 102as well as observers and estimators generated by first processing device104.

In this way, input and output activity within system 100 may be used, atleast in part, to construct functional and structural models representedby second processing device 106. More specifically, the artificialneural networks created by second processing device 106 may be modifiedand configured based on activity of the human brain. In this way, theartificial neural networks created by second processing device 106 maybe specifically configured based on behaviors and processing patterns ofthe human mind.

In some embodiments, second processing device 106 is further configuredto construct artificial neural networks that alter one or moreparameters of treatment or administration of brain stimulation on anindividual, based on one or more received inputs. In some embodiments,such parameters of treatment may include the schedule or nature foradministration of brain stimulation treatment, as well as the timeframe, duration, frequency, and/or strength of treatment following abrain injury. In some embodiments, the one or more received inputs maybe received information regarding the efficacy or likelihood of successor mitigation of symptoms of a brain injury in relation to the timeframe of administration of brain stimulation treatment. In someembodiments, the artificial neural networks are modified or adjustedbased on updated datasets or additional input information correspondingto the timing of treatments and the results of treatments. For example,such input information may be training data that is utilized by neuralnetworks upon which one or more machine learning algorithms areexecuted, such that the neural networks learn improved techniques forhow to more effectively administer brain stimulation treatments,including adjusted timing, duration, frequency, and/or nature oftreatments.

In various embodiments, second processing device 106 is furtherconfigured to implement system identification, dimensional modeling, anddimension reduction to identifying preferred models. Furthermore, secondprocessing device 106 may be configured to implement identification ofone or more brain states to be controlled, as may be determined based onactuation sensitivity and efficacy. In various embodiments, secondprocessing device 106 is configured to implement identification of themost sensitive brain states to be measured. Such identification mayidentify the most sensitive and granular measurement that identifies thebrain state changes. In some embodiments, second processing device 106is configured to implement multi-input, multi-output measurement andactuation. In some embodiments, second processing device 106 is furtherconfigured to multi-time scale modelling (to capture the slow system andfast system dynamic accurately). In various embodiments, secondprocessing device 106 is also configured to implement one or more neuralnet basis functions that may be configured and or generated based onactivity of the brain. For example, such functions may include spikefunctions, multi-input-coevolution triggered firing (such as coherence,synchrony, coupling, correlation of two waveforms triggering a cellfiring).

In some embodiments, second processing device 106 is further configuredto capture or record results of treatments and various parameters andinformation related to treatments, including pieces of informationrelating to the efficacy of treatments and/or the mitigation or lack ofmitigation of one or more symptoms of an injury. In some embodiments,second processing device 106 is also configured to capture or record thetime frames of various treatments of individuals. In some embodiments,time frames include the timing, duration, frequency, and/or nature oftreatments following the event of an injury to the brain. In someembodiments, second processing device 106 is configured to retrieveexisting or historical information or datasets relating to treatments,such as information relating to the efficacy of treatments or time frameof treatments, including timing, duration, frequency, and or nature oftreatments following the event of an injury to the brain.

While first processing device 104 and second processing device 106 havebeen described separately, in various embodiments, both first processingdevice 104 and second processing device 106 are implemented in a singleprocessing device.

Accordingly, a single processing device may be specifically configuredto implement first processing device 104 and second processing device106.

System 100 further includes controller 108 configured to implement andcontrol closed loop control of treatments and cognitive enhancements. Invarious embodiments, controller 108 is communicatively coupled withinterface 102, first processing device 104, and second processing device106. Accordingly, controller 108 is configured to receive inputs fromvarious other system components, and generate outputs based, at least inpart on such inputs. As will be discussed in greater detail below, suchoutputs may be used to provide actuations to the brain coupled withinterface 102. For example, outputs generated by controller 108 may beused to stimulate the brain via one or more components of interface 102.In this way, controller 108 may provide stimuli to the brain viainterface 102, may receive measurements, parameter information, andmodel information via other components such as first processing device104 and second processing device 106, and may generate updated stimulibased on such received information.

Thus, according to some embodiments, controller 108 is configured toimplement multi-input, multi-output feedback control. Controller 108 mayalso be configured to implement loop shaping optimized feedback control.In a specific example, controller 108 is configured to implement modelreference adaptive control. Furthermore, controller 108 may beconfigured to implement cognitive enhancement trajectory control. Invarious embodiments, controller 108 is configured to implement enhancedcontrol in which one or more parameters of a treatment may be enhancedby increasing its efficiency and/or effect. For example, an input orstimulation may be reduced to implement a same enhancement, a durationof stimulations may be reduced but still reach desired improvement, anda path of recovery may be made more efficient. In this way, an amount ofstimulation, which may be a combination of amplitude and duration, maybe reduced while still obtaining a desired effect, thus increasing theefficacy of the treatment and reducing overstimulation. In variousembodiments, controller 108 is configured to implement a geneticalgorithm to identify a particular stimulation pathway that reduces anamount of stimulation.

In some embodiments, controller 108 is configured to implement combinedcontrol of pharmacological and stimulation inputs. Accordingly,controller 108 may be configured to modify stimulation inputs based onan expected effect of one or more pharmacological agents that may beadministered in conjunction with the stimulation. In this way,controller 108 may modify and control administration of stimuli viainterface 102 based on an identified pharmacological regimen. In variousembodiments, controller 108 is configured to implement game theoreticstrategy-based treatments. In some embodiments, controller 108 isconfigured to implement real time measurement/estimation and control.

In various embodiments, controller 108 is configured to receive areference signal which may be used, at least in part, to generate ormodify the stimuli provided via interface 102. In various embodiments,the reference signal may be a previously determined signal or patternthat may represent a reference level or pattern of neural activity. Insome embodiments, the reference signal may be generated based on a“negative” model. In a specific example, such a negative model may be afunctional and/or structural model that is generated based on a reverseor inversion of one or more of the models created and stored by secondprocessing device 106. Accordingly, such a negative model may begenerated by second processing device 106, and the reference signal maybe generated by second processing device 106 and received at controller108.

As will be discussed in greater detail below, the above-describedcomponents of system 100 may be specifically configured to provide oneor more particular applications. For example, system 100 may beconfigured to provide bio-signal interpretation for status monitoringand diagnostics. Accordingly, the brain state information observed andderived by at least first processing device 104 and second processingdevice 106 may be used to identify brain states, the onset of particularbrain states, and particular transitions between brain states. Suchevents may be detected, and notifications may be generated by, forexample, first processing device 104, second processing device 106, orcontroller 108. Such notifications may be provided to other entities, orclient devices 110, that may be involved in status monitoring anddiagnostics, such as computing and mobile devices associated withmedical professionals, or a user's in-home medical system.

In various implementations, client device 110 may be any one of variouscomputing devices such as laptop or desktop computers, smartphones,personal digital assistants, portable media players, tablet computers,or other appropriate computing devices that can be used to communicateover a global or local network, such as the Internet. In variousembodiments, client device 110 may be configured to communicate with thefirst processing device 104, second processing device 106, and/orcontroller 108.

In some embodiments, client device 110 may receive information from thevarious other components, such as device status, performance or functioninformation, predictive models, brain state parameters, diagnosticinformation, mediation procedures, etc. For example, the brain stateparameters generated by first processing device 104 may be transmittedto client device 110. In some embodiments, functional and structuralmodels of the brain generated by second processing device 106 may betransmitted to client device. As another example, particular controlsignals may be communicated to controller 108 form client device 110 tobe sent to interface 102 or prosthetic 112. In some embodiments, clientdevice 110 may also be configured to directly communicate with interface102 or prosthetic 112, for example to transmit control signals ormonitor device status.

Moreover, system 100 may be configured to provide augmented reality(AR)-virtual reality (VR) for cognitive enhancements and side-effectremoval. For example, brain state and associated parameter detection maybe used to identify the onset of particular cognitive states, such asmotion sickness. When detected, or even when the onset is detected,controller 108 may generate one or more stimuli, which may be visualstimuli, tactile stimuli, and/or electric stimuli, to alleviate thedetected cognitive state. In this way, motion sickness associated withVR may be alleviated. In various embodiments, brain stimulation may beused to enhance sensory perception associated with AR and VR.Accordingly, one or more stimuli may be provided via interface 102 wheresuch stimuli are determined based on the AR or VR program, and suchstimuli may enhance or improve the experience of the AR or VR program.

In various embodiments, system 100 is configured to provide cognitiveand behavioral modulation specific to a particular psychological orneurophysiological condition. For example, a condition such asdepression may be characterized by a frozen brain state, or a brainstate that does not oscillate as a normal brain would. In variousembodiments, system 100 may be configured to identify a particular brainstate, identify that it is “frozen” (has not changed over a designatedperiod of time), and generate one or more control signals that areconfigured to stimulate the user's brain and change the brain state ofthe user by virtue of the stimulation to alleviate the depression. Inanother example, such generation of control signals may be used toalleviate or manage pain, as may be applicable with chronic pain. Suchcontrol signals associated with pain management may be implemented usingmulti-modal (multi-sensory) stimulation. In yet another example,generation of control signals may be utilized for stimulation basedreversal of ‘minimally-conscious’ or ‘comatose’ brain states of users.In an additional example, such control signals may be used to mitigateor alleviate age based cognitive decline. Accordingly, stimuli may beprovided via interface 102 to stimulate areas having diminished activitydue to the process of aging.

In some embodiments, system 100 may be configured to provide system andpathology specific functional brain models, as may be utilized inpharmacological applications. Accordingly, as discussed above, the useof pharmacological agents may be identified, and models may be updatedin response to such pharmacological agents being identified. In thisway, administration of stimuli via interface 102 may be modified andupdated based on the application of a pharmacological agent to a user.More specifically, such modifications may be implemented based on theidentification of the use of pharmacological agents, as well as directlymeasured neural activity of the user during the treatment process.

In another example, system 100 may be configured to provide model basedadaptive control paradigms for feedback treatments and cognitiveenhancements. Accordingly, as discussed above, treatments and cognitiveenhancements may be provided with adaptive closed loop control thatenables the modification and updating of such treatments and cognitiveenhancements based on directly measured neural activity over the courseof the treatments and cognitive enhancements.

In various embodiments, system 100 may be configured to provide smartembedded prosthetics (e.g. speech decoder, motor control). Accordingly,signals generated by controller 108 may be used to control one or moreembedded prosthetics, such as prosthetic 112. In some embodiments,prosthetic 112 may be an implanted stimulator that may be activated bycontroller 108 in response to the detection or identification of one ormore brain states or parameters associated with such brain states. Insome embodiments, prosthetic 112 may be a component of interface 102 orcommunicatively coupled to interface 102.

In a specific example, prosthetic 112 may be a stimulator configured toprevent epileptic episodes. In this example, controller 108 may identifya brain state corresponding to an onset of an epileptic seizure, and mayprovide a signal to prosthetic 112 that activates prosthetic 112 toprevent the seizure. For example, prosthetic 112 may comprise amulti-channel closed-loop neural-prosthetic system-on-chip (SoC)configured for real-time intracranial electroencephalogram (iEEG)acquisition, seizure detection, and electrical stimulation in order tosuppress epileptic seizures.

In some embodiments, system 100 may be configured to provide a toolboxconfigured to support estimation, modeling, and control. In anotherexample, system 100 may be configured to provide a bio operating system(BoS) framework for biological measurement and an actuation controlplatform. As discussed above, such an operating system may beimplemented on a variety of platforms including a mobile platform suchas mAndroid. In some embodiments, system 100 may be configured toprovide task specific cognitive enhancements, such as FAA monitoringagents, and fighter pilot related tasks.

System 100 and its respective components may be implemented in a varietyof contexts. For example, system 100 may be implemented in a clinicalsetting that may include an examination room, an operating room, or anemergency room. Moreover, system 100 may be implemented in a user's homethus providing in-home monitoring, diagnostic, and treatment.Furthermore, portions of system 100 may be implemented in a firstlocation while other portions are implemented in a second location. Forexample, interface 102 may be located at a user's home, while firstprocessing device 104, second processing device 106, and controller 108are implemented remotely, as may be the case when implemented at ahospital.

Furthermore, system 100 may be implemented across multiple users. Forexample, system 100 may include multiple interfaces that are coupledwith multiple brains. In this way, measurements may be made frommultiple users, and stimuli may be provided to multiple users. In oneexample, measurements from a first user may be used to generate andprovide stimuli to a second user. In this way, synchronization of atleast part of a brain state may be implemented across multiple users.

FIG. 2 illustrates an example an example of a flow chart of a method forproviding closed loop control in treatments and cognitive enhancements,implemented in accordance with some embodiments. As discussed above,various components of system 100 may be configured to implement modelingand closed loop management of treatments and therapies provided to auser.

Accordingly, method 200 may commence with operation 202 during whichmeasurements are obtained from a brain via an interface. Themeasurements may represent neural activity over a particular period oftime, or temporal window, and may be obtained via components of a braininterface. Such measurements may be acquired and stored in a memory.

Method 200 may proceed to operation 204 during which parametersassociated with brain states are generated. As similarly discussedabove, such parameters may include observers and estimators associatedwith brain states as well as identification of the brain statesthemselves. Such parameters may be generated by a first processingdevice and may be stored in a local memory.

Method 200 may proceed to operation 206 during which functional andstructural models are generated based, at least in part, on themeasurements and the parameters. As discussed above, such models mayemulate functions, tasks, and components of the user's brain, and may beconfigured based on the brain's activity and behavior. Such models mayalso be configured based on previously obtained reference data.

Method 200 may proceed to operation 208 during which a control signal isgenerated based, at least in part on, on the measurements and themodels. Accordingly, one or more control signals may be generated basedon recent neural activity represented by measurement data, and alsobased on expected or desired effects as determined based on the models.In this way, specific control signals may be generated to implement aparticular cognitive modulation that is specifically configured for theuser. Moreover, as discussed above and in greater detail below, suchcontrol signals may be generated and implemented in a closed loopmanner.

Method 200 may proceed to operation 210 during which the control signalis provided to the interface. Accordingly, the control signal may beprovided to the interface which may generate one or more stimuli basedon the control signal. For example, such stimuli may include electricalstimuli, visual stimuli, aural stimuli, and/or tactile stimuli that mayhave parameters, such as amplitude and duration, determined based on thecontrol signal.

Method 200 may proceed to operation 212 during which it may bedetermined if additional measurements should be made. In variousembodiments, such a determination may be made based on a current state,or in response to one or more conditions. For example, if a particulartherapeutic regimen is implemented, a series of measurement may be madeaccording to a predetermined schedule, and such measurements may bestepped through utilizing a state machine. If it is determined thatadditional measurements should be made, method 200 may return tooperation 202. If it is determined that no additional measurementsshould be made, method 200 may terminate.

FIG. 3 illustrates an example of a flow chart of a method for statusmonitoring and diagnostics, implemented in accordance with someembodiments. As discussed above, various components of system 100 may beconfigured to identify particular brain states of a user based on themeasured neural activity of a user, and may be further configured togenerate notification messages or other status monitoring and diagnosticinformation.

Method 300 may commence with operation 302 during which measurements areobtained from a brain via an interface. As similarly discussed above,such measurements may represent neural activity over a particular periodof time, or temporal window, and may be obtained via components of abrain interface. In various embodiments, the neural activity may beassociated with a particular cognitive or therapeutic condition, and theacquisition of such measurements may be implemented responsive todetection of an event or one or more other conditions.

Method 300 may proceed to operation 304 during which parametersassociated with brain states are generated. As similarly discussedabove, such parameters may include observers and estimators associatedwith brain states as well as identification of the brain statesthemselves. As also discussed above, such parameters may be utilized toidentify one or more brain states associated with a particular cognitiveor therapeutic condition.

Method 300 may proceed to operation 306 during which functional andstructural models are generated based, at least in part, on themeasurements and the parameters. As discussed above, such models mayemulate functions, tasks, and components of the user's brain, and may beconfigured based on the brain's activity and behavior. Such models mayalso be configured based on previously obtained reference data and/orpreviously obtained measurements.

Method 300 may proceed to operation 308 during which one or morenotification messages and diagnostic information are generated based, atleast in part on, on the measurements and the models. Accordingly,messages and other diagnostic information may be generated based onrecent neural activity represented by measurement data and the generatedmodels. In this way, specific identified parameters and brain states maybe mapped to therapeutic conditions, or the onset of such conditions,and messages may be generated that provide one or more other entitieswith a notification of the occurrence of such brain states. In this way,diagnostic messages identifying the onset of particular brain states andtherapeutic conditions of a user may be generated and sent to one ormore other entities. As will be discussed in greater detail below, suchother entities may be monitoring entities.

Accordingly, method 300 may proceed to operation 310 during which theone or more notification messages and diagnostic information areprovided to one or more entities. In various embodiments, the one ormore entities may be computing devices associated with monitoringentities such as medical professionals, or a user's account. In thisway, notification and diagnostic messages may be automatically generatedand sent to various entities responsive to the neural activity of theuser.

Method 300 may proceed to operation 312 during which during which it maybe determined if additional measurements should be made. As similarlydiscussed above, such a determination may be made based on a currentstate, or in response to one or more conditions. For example, additionalmeasurements may be made responsive to an input provided by anotherentity, which may be a monitoring entity, or according to apredetermined schedule. If it is determined that additional measurementsshould be made, method 300 may return to operation 302. If it isdetermined that no additional measurements should be made, method 300may terminate.

FIG. 4 illustrates an example of a flow chart of a method for control ofa prosthetic, implemented in accordance with some embodiments. Asdiscussed above, various components of system 100 may be configured tocontrol various other entities, such as prosthetics, which may beembedded prosthetics, based on the measured neural activity of a user.

Method 400 may commence with operation 402 during which measurements areobtained from a brain via an interface. As similarly discussed above,such measurements may represent neural activity over a particular periodof time, or temporal window, and may be obtained via components of abrain interface. In various embodiments, the neural activity may beassociated with a particular prosthetic, and the acquisition of suchmeasurements may be implemented responsive to detection of an event orone or more other conditions.

Method 400 may proceed to operation 404 during which parametersassociated with brain states are generated. As similarly discussedabove, such parameters may include observers and estimators associatedwith brain states as well as identification of the brain statesthemselves. As also discussed above, such parameters may be utilized toidentify one or more brain states associated with the prosthetic. Asdiscussed above, for a prosthetic associated with epileptic seizures,parameters may be generated that may be utilized to identify brain statesignatures indicative of an onset of an epileptic seizure.

Method 400 may proceed to operation 406 during which functional andstructural models are generated based, at least in part, on themeasurements and the parameters. As discussed above, such models mayemulate functions, tasks, and components of the user's brain, and may beconfigured based on the brain's activity and behavior. Such models mayalso be configured based on previously obtained reference data and/orpreviously obtained measurements.

Method 400 may proceed to operation 408 during which a control signal isgenerated based, at least in part on, on the measurements and themodels. Accordingly, one or more control signals may be generated basedon recent neural activity represented by measurement data. In this way,specific control signals may be generated to implement a particularfunctionality of the prosthetic that is responsive to the identifiedbrain states.

Method 400 may proceed to operation 410 during which the control signalis provided to the prosthetic. Accordingly, the control signal may beprovided directly to the prosthetic from a controller, or may beprovided via an interface. Moreover, the prosthetic may implement one ormore operations responsive to receiving the control signal. In this way,the control signal may be implemented by the prosthetic andfunctionalities provided by the prosthetic may be implemented.

Method 400 may proceed to operation 412 during which it may bedetermined if additional measurements should be made. As similarlydiscussed above, such a determination may be made based on a currentstate, or in response to one or more conditions. For example, additionalmeasurements may be made responsive to the activation of the prostheticor according to a predetermined schedule. If it is determined thatadditional measurements should be made, method 400 may return tooperation 402. If it is determined that no additional measurementsshould be made, method 400 may terminate.

FIG. 5 illustrates an example an example of a flow chart of a method forproviding mediation of traumatic brain injury, implemented in accordancewith some embodiments. As discussed above, various components of system100 may be configured to implement providing mediation of traumaticbrain injury.

Accordingly, method 500 may commence with operation 502 during whichmeasurements are obtained from a brain via an interface. In someimplementations, the measurements are obtained from a brain of a userwith a neural condition. In some implementations, the neural conditionis a traumatic brain injury. In some embodiments, the neural conditionmay be, for example, epilepsy, depression, or any other condition withrespect to the brain. The measurements may represent neural activityover a particular period of time, or temporal window, and may beobtained via components of a brain interface. Such measurements may beacquired and stored in a memory.

Method 500 may proceed to operation 504 during which brain stateparameters, or parameters associated with brain states, are generated.As similarly discussed above, such parameters may include observers andestimators associated with brain states as well as identification of thebrain states themselves. Such parameters may be generated by a firstprocessing device and may be stored in a local memory.

Method 500 may proceed to operation 506 during which functional andstructural models are generated based, at least in part, on themeasurements and the parameters. As discussed above, such models mayemulate functions, tasks, and components of the user's brain, and may beconfigured based on the brain's activity and behavior. Such models mayalso be configured based on previously obtained reference data.

Method 500 may process to operation 508 during which a procedure formediation is determined. In some embodiments, the procedure formediation is configured to reduce one or more symptoms of a neuralcondition, such as traumatic brain injury of the user. In someembodiments, the models of the brain are used to determine the procedurefor mediation. In some embodiments, training data is additionally used,with the training data consisting of one or more mediation data points.In some embodiments, the mediation data points include, for example, oneor more models of additional users' brains, or one or more previousprocedures for mediation of traumatic brain injury. In some embodiments,the training data is used in conjunction with machine learningalgorithms or artificial intelligence modalities. In some embodiments,the machine learning algorithms or artificial intelligence modalitiesdetermine a procedure for mediation based on one or more data pointswithin the training data that are determined to suggest a procedure formediation that alleviates one or more symptoms of traumatic braininjury. For example, if one or more data points of previous mediation oftraumatic brain injuries of users suggest, via machine learningalgorithms that process and analyze the data points, a method that leadsto optimal mediation of brain injury, then a procedure for mediation ofthe user's traumatic brain injury can be determined based on those datapoints.

Method 500 may proceed to operation 510 during which the procedure formediation is provided to one or more entities. In some embodiments, theone or more entities may be, for example, client devices, user devices,first processing device 104, second processing device 106, controller108, one or more external applications or websites, or some combinationthereof.

Method 500 may proceed to operation 512 during which a control signal isgenerated based, at least in part, on the brain state parameters and themodels to determine a procedure for mediation. Accordingly, one or morecontrol signals may be generated based on recent neural activityrepresented by measurement data, and also based on expected or desiredeffects as determined based on the models. In this way, specific controlsignals may be generated to implement a particular cognitive modulationthat is specifically configured for the user. Moreover, as discussedabove and in greater detail below, such control signals may be generatedand implemented in a closed loop manner.

Method 500 may proceed to operation 514 during which it may bedetermined if additional measurements should be made. In variousembodiments, such a determination may be made based on a current state,or in response to one or more conditions. For example, if a particulartherapeutic regimen is implemented, a series of measurement may be madeaccording to a predetermined schedule, and such measurements may bestepped through utilizing a state machine. If it is determined thatadditional measurements should be made, method 500 may return tooperation 502. If it is determined that no additional measurementsshould be made, method 500 may terminate.

FIG. 6 illustrates an example of a computer system that can be used withvarious embodiments. For instance, the computer system 600 can be usedto implement first processing device 104, second processing device 106,controller 108, and/or prosthetic 112 according to various embodimentsdescribed above. In addition, the computer system 600 shown canrepresent a computing system on a mobile device or on a computer orlaptop, etc., such as client device 110. According to particular exampleembodiments, a system 600 suitable for implementing particularembodiments of the present invention includes a processor 601, a memory603, an interface 611, and a bus 615 (e.g., a PCI bus).

When acting under the control of appropriate software or firmware, theprocessor 601 is responsible for tasks such as closed loop control.Various specially configured devices can also be used in place of aprocessor 601 or in addition to processor 601. The completeimplementation can also be done in custom hardware.

The interface 611 may include separate input and output interfaces, ormay be a unified interface supporting both operations. The interface 611is typically configured to send and receive data packets or datasegments over a network. Particular examples of interfaces the devicesupports include Ethernet interfaces, frame relay interfaces, cableinterfaces, DSL interfaces, token ring interfaces, and the like.

In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as packet switching, media control and management.

According to particular example embodiments, the system 600 uses memory603 to store data and program instructions and maintain a local sidecache. The program instructions may control the operation of anoperating system and/or one or more applications, for example. Thememory or memories may also be configured to store received metadata andbatch requested metadata.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present inventionrelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include hard disks,floppy disks, magnetic tape, optical media such as CD-ROM disks andDVDs; magneto-optical media such as optical disks, and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory devices (ROM) and programmable read-only memorydevices (PROMs). Examples of program instructions include both machinecode, such as produced by a compiler, and files containing higher levelcode that may be executed by the computer using an interpreter.

While the present disclosure has been particularly shown and describedwith reference to specific embodiments thereof, it will be understood bythose skilled in the art that changes in the form and details of thedisclosed embodiments may be made without departing from the spirit orscope of the invention. Specifically, there are many alternative ways ofimplementing the processes, systems, and apparatuses described. It istherefore intended that the invention be interpreted to include allvariations and equivalents that fall within the true spirit and scope ofthe present invention. Moreover, although particular features have beendescribed as part of each example, any combination of these features oradditions of other features are intended to be included within the scopeof this disclosure. Accordingly, the embodiments described herein are tobe considered as illustrative and not restrictive.

1-20. (canceled)
 21. A system comprising: an interface configured toobtain a plurality of measurements from a brain of a user with atraumatic brain injury; a processing device configured to: generate aplurality of brain state parameters characterizing one or more featuresof at least one brain state of the user, and train a neural network togenerate one or more models of the brain of the user, wherein the neuralnetwork is trained using training data generated, at least in part, fromone or more mediation data points, the plurality of brain stateparameters, and the plurality of measurements, wherein a procedure formediation is determined using at least the one or more models, theprocedure for mediation configured to reduce one or more symptoms of thetraumatic brain injury; and a controller configured to generate one ormore control signals for performing an operation on the brain of theuser to suppress epileptic seizures, wherein the one or more controlsignals are generated based on the procedure for mediation.
 22. Thesystem of claim 21, wherein the plurality of measurements representsneural activity of the brain over a given period of time.
 23. The systemof claim 21, wherein the plurality of brain state parameters includesdeterministic and stochastic observers and estimators of one or morebrain states.
 24. The system of claim 21, wherein the plurality of brainstate parameters includes a learning estimator model configured toestimate effects of behavioral or functional responses.
 25. The systemof claim 21, wherein the processing device is further configured to:generating diagnostic information based on the one or more models; andtransmitting a notification message including the diagnostic informationto one or more entities.
 26. The system of claim 21, further comprisingan embedded prosthetic device implanted in the user, wherein theembedded prosthetic device is configured to perform the operation on thebrain of the user based on the one or more control signals.
 27. Thesystem of claim 26, wherein the embedded prosthetic device iscommunicatively coupled to the interface.
 28. The system of claim 26,wherein the controller is further configured to update the controlsignals transmitted to the embedded prosthetic device based on updatedmeasurements obtained by the interface after the operation is performedon the brain of the user.
 29. The system of claim 26, wherein theembedded prosthetic device is activated by the controller in response todetection or identification of one or more of the plurality of brainstate parameters.
 30. A method comprising: obtaining a plurality ofmeasurements from a brain of a user with a traumatic brain injury,wherein the plurality of measurements are obtained by an interface;generating, via a processing device, a plurality of brain stateparameters characterizing one or more features of at least one brainstate of the user; training, via the processing device, a neural networkto generate one or more models of the brain of the user, wherein theneural network is trained using training data generated, at least inpart, from one or more mediation data points, the plurality of brainstate parameters, and the plurality of measurements, wherein a procedurefor mediation is determined using at least the one or more models, theprocedure for mediation configured to reduce one or more symptoms of thetraumatic brain injury; and generating, via a controller, one or morecontrol signals for performing an operation on the brain of the user tosuppress epileptic seizures, wherein the one or more control signals aregenerated based on the procedure for mediation.
 31. The method of claim30, wherein the plurality of measurements represent neural activity ofthe brain over a given period of time.
 32. The method of claim 30,wherein the plurality of brain state parameters includes deterministicand stochastic observers and estimators of one or more brain states. 33.The method of claim 30, wherein the plurality of brain state parametersincludes a learning estimator model configured to estimate effects ofbehavioral or functional responses.
 34. The method of claim 30, furthercomprising: generating diagnostic information based on the one or moremodels; and transmitting a notification message including the diagnosticinformation to one or more entities.
 35. The method of claim 30, furthercomprising performing, via an embedded prosthetic device implanted inthe user, the operation on the brain of the user based on the one ormore control signals.
 36. The method of claim 35, wherein the embeddedprosthetic device is communicatively coupled to the interface.
 37. Themethod of claim 35, further comprising updating the control signalstransmitted to the embedded prosthetic device based on updatedmeasurements obtained by the interface after the operation is performedon the brain of the user.
 38. The method of claim 35, wherein theembedded prosthetic device is activated by the controller in response todetection or identification of one or more of the plurality of brainstate parameters.
 39. A non-transitory computer readable medium storingone or more programs configured for execution by a computer, the one ormore programs comprising instructions for: obtaining a plurality ofmeasurements from a brain of a user with a traumatic brain injury,wherein the plurality of measurements are obtained by an interface;generating, via a processing device, a plurality of brain stateparameters characterizing one or more features of at least one brainstate of the user; training, via the processing device, a neural networkto generate one or more models of the brain of the user, wherein theneural network is trained using training data generated at least inpart, from one or more mediation data points, the plurality of brainstate parameters, and the plurality of measurements, wherein a procedurefor mediation is determined using at least the one or more models, theprocedure for mediation configured to reduce one or more symptoms of thetraumatic brain injury; and generating, via a controller, one or morecontrol signals for performing an operation on the brain of the user tosuppress epileptic seizures, wherein the one or more control signals aregenerated based on the procedure for mediation.
 40. The non-transitorycomputer readable medium of claim 39, wherein the one or more programsfurther comprise instructions for performing, via an embedded prostheticdevice implanted in the user, the operation on the brain of the userbased on the one or more control signals.